{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print 'Training data shape: ', X_train.shape\n",
    "print 'Training labels shape: ', y_train.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'Test labels shape: ', y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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etnHzdO3MGl1xAsf8oXn6m24d5FlO1HW8p9VtM1S+ZQu8j1FvboFShTaxJR01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mM7tmM7tmN7BPvCIk/NBt1OlJDw+Jbj+/4DkafitvtJMxbymok8GUAVt92HRZ6E/Qf/Ld/6YWZg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\nmWqpsHGKFNfBZIDRkA7FIkkdBUe700HM2V5SSedWjaLQ8QC1O20EAf++3UZ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snvMjH7qs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M3+B7194zn5NJxk+toNjhkeDvT4UJAgyScwuw6Qjp2QFiwqp1mMmWyT5XiIiA34qJfjvEu6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\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16f4H/WOTLzQehxgzeZ8Q0oQwfd+Hx8+IwnaknORZhpQVNcuyTUVbJagYYs+HYXuLWe21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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a4d505d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print 'Train data shape: ', X_train.shape\n",
    "print 'Train labels shape: ', y_train.shape\n",
    "print 'Validation data shape: ', X_val.shape\n",
    "print 'Validation labels shape: ', y_val.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'Test labels shape: ', y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print 'Training data shape: ', X_train.shape\n",
    "print 'Validation data shape: ', X_val.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'dev data shape: ', X_dev.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 130.64189796  135.98173469  132.47391837  130.05569388  135.34804082\n",
      "  131.75402041  130.96055102  136.14328571  132.47636735  131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a4a091f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print mean_image[:10] # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print X_train.shape, X_val.shape, X_test.shape, X_dev.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 8.653964\n",
      "(8.6539635334306073, array([[ -1.61490260e+01,  -2.53225676e+01,  -1.15224852e+01, ...,\n",
      "          3.16872874e+00,  -1.01493644e+01,  -2.33307037e+01],\n",
      "       [ -2.68112147e+01,  -2.04239269e+01,  -1.08117992e+01, ...,\n",
      "          2.51899551e+00,  -1.85294978e+01,  -2.51590775e+01],\n",
      "       [ -4.54425904e+01,  -2.22398957e+01,   2.83657502e+00, ...,\n",
      "          2.27827739e+00,  -3.88891387e+01,  -3.08518269e+01],\n",
      "       ..., \n",
      "       [ -1.24102952e+01,   1.86455216e+00,  -1.46307125e+01, ...,\n",
      "          4.00263869e+00,   2.58666807e+01,  -1.08739135e+01],\n",
      "       [ -1.93096754e+01,  -9.17959829e+00,  -1.25888914e+00, ...,\n",
      "          1.17244703e+01,   8.38276486e+00,  -1.45419314e+01],\n",
      "       [ -1.76000001e-01,   4.00000102e-03,  -2.08000001e-01, ...,\n",
      "          1.63999999e-01,  -8.20000003e-02,   1.59999999e-01]]))\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import *\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "print 'loss: %f' % (loss, )\n",
    "print (loss, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 9.888655 analytic: 9.888655, relative error: 1.810828e-11\n",
      "numerical: -9.380614 analytic: -9.433127, relative error: 2.791247e-03\n",
      "numerical: 2.496177 analytic: 2.496177, relative error: 2.386606e-11\n",
      "numerical: 28.115393 analytic: 28.053662, relative error: 1.099031e-03\n",
      "numerical: -16.820123 analytic: -16.820123, relative error: 2.193309e-11\n",
      "numerical: 25.785707 analytic: 25.796834, relative error: 2.157193e-04\n",
      "numerical: 26.950857 analytic: 26.882407, relative error: 1.271526e-03\n",
      "numerical: 29.009947 analytic: 29.009947, relative error: 1.138089e-11\n",
      "numerical: -18.358137 analytic: -18.358137, relative error: 1.145141e-11\n",
      "numerical: 4.765335 analytic: 4.765335, relative error: 3.248128e-11\n",
      "numerical: 11.597624 analytic: 11.597624, relative error: 1.184200e-12\n",
      "numerical: 2.130162 analytic: 2.127499, relative error: 6.254378e-04\n",
      "numerical: 1.798213 analytic: 1.798213, relative error: 1.086375e-10\n",
      "numerical: 11.689602 analytic: 11.757548, relative error: 2.897815e-03\n",
      "numerical: 0.662772 analytic: 0.662772, relative error: 1.354531e-10\n",
      "numerical: -12.504309 analytic: -12.504309, relative error: 1.095507e-11\n",
      "numerical: 13.029169 analytic: 13.029169, relative error: 3.188989e-12\n",
      "numerical: -2.726514 analytic: -2.710404, relative error: 2.963015e-03\n",
      "numerical: 19.976953 analytic: 19.976953, relative error: 4.559033e-12\n",
      "numerical: 25.410880 analytic: 25.410880, relative error: 3.093872e-12\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 1e2)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 1e2)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *fill this in.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 8.653964e+00 computed in 0.181691s\n",
      "Vectorized loss: 8.653964e+00 computed in 0.034966s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Naive loss: %e computed in %fs' % (loss_naive, toc - tic)\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic)\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print 'difference: %f' % (loss_naive - loss_vectorized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.183099s\n",
      "Vectorized loss and gradient: computed in 0.016179s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Naive loss and gradient: computed in %fs' % (toc - tic)\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Vectorized loss and gradient: computed in %fs' % (toc - tic)\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print 'difference: %f' % difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print 'That took %fs' % (toc - tic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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2iFgYEY9FxMMRMb2yb4+IuDsi5kXE1Ijo3+74qyJifkTMiYjTq11feyNHwoIFtbyiJElS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\ncPjhRVcmSaoXhraS1azGtm5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JIUcAH92eN0bEtiYBv+ptF0rphO05fwF2anLMiPDvbalJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a490014d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.369286\n",
      "validation accuracy: 0.373000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print 'training accuracy: %f' % (np.mean(y_train == y_train_pred), )\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print 'validation accuracy: %f' % (np.mean(y_val == y_val_pred), )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1e-07, 50000.0), (1e-07, 100000.0), (5e-05, 50000.0), (5e-05, 100000.0)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [5e4, 1e5]\n",
    "\n",
    "com = [(x,y) for x in learning_rates for y in regularization_strengths]\n",
    "com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1e-07, 50000.0)\n",
      "validation accuracy: 0.391000\n",
      "(1e-07, 1000.0)\n",
      "validation accuracy: 0.308000\n",
      "(1e-07, 100.0)\n",
      "validation accuracy: 0.299000\n",
      "(1e-07, 10.0)\n",
      "validation accuracy: 0.310000\n",
      "(1e-07, 1)\n",
      "validation accuracy: 0.317000\n",
      "(1e-07, 0.1)\n",
      "validation accuracy: 0.303000\n",
      "(5e-06, 50000.0)\n",
      "validation accuracy: 0.171000\n",
      "(5e-06, 1000.0)\n",
      "validation accuracy: 0.288000\n",
      "(5e-06, 100.0)\n",
      "validation accuracy: 0.326000\n",
      "(5e-06, 10.0)\n",
      "validation accuracy: 0.321000\n",
      "(5e-06, 1)\n",
      "validation accuracy: 0.340000\n",
      "(5e-06, 0.1)\n",
      "validation accuracy: 0.300000\n",
      "(1e-06, 50000.0)\n",
      "validation accuracy: 0.265000\n",
      "(1e-06, 1000.0)\n",
      "validation accuracy: 0.363000\n",
      "(1e-06, 100.0)\n",
      "validation accuracy: 0.355000\n",
      "(1e-06, 10.0)\n",
      "validation accuracy: 0.347000\n",
      "(1e-06, 1)\n",
      "validation accuracy: 0.351000\n",
      "(1e-06, 0.1)\n",
      "validation accuracy: 0.333000\n",
      "(5e-05, 50000.0)\n",
      "validation accuracy: 0.051000\n",
      "(5e-05, 1000.0)\n",
      "validation accuracy: 0.213000\n",
      "(5e-05, 100.0)\n",
      "validation accuracy: 0.301000\n",
      "(5e-05, 10.0)\n",
      "validation accuracy: 0.314000\n",
      "(5e-05, 1)\n",
      "validation accuracy: 0.287000\n",
      "(5e-05, 0.1)\n",
      "validation accuracy: 0.319000\n",
      "(1e-05, 50000.0)\n",
      "validation accuracy: 0.223000\n",
      "(1e-05, 1000.0)\n",
      "validation accuracy: 0.209000\n",
      "(1e-05, 100.0)\n",
      "validation accuracy: 0.248000\n",
      "(1e-05, 10.0)\n",
      "validation accuracy: 0.303000\n",
      "(1e-05, 1)\n",
      "validation accuracy: 0.318000\n",
      "(1e-05, 0.1)\n",
      "validation accuracy: 0.335000\n",
      "(0.0005, 50000.0)\n",
      "validation accuracy: 0.087000\n",
      "(0.0005, 1000.0)\n",
      "validation accuracy: 0.158000\n",
      "(0.0005, 100.0)\n",
      "validation accuracy: 0.208000\n",
      "(0.0005, 10.0)\n",
      "validation accuracy: 0.249000\n",
      "(0.0005, 1)\n",
      "validation accuracy: 0.297000\n",
      "(0.0005, 0.1)\n",
      "validation accuracy: 0.331000\n",
      "lr 1.000000e-07 reg 1.000000e-01 train accuracy: 0.302306 val accuracy: 0.303000\n",
      "lr 1.000000e-07 reg 1.000000e+00 train accuracy: 0.307837 val accuracy: 0.317000\n",
      "lr 1.000000e-07 reg 1.000000e+01 train accuracy: 0.308429 val accuracy: 0.310000\n",
      "lr 1.000000e-07 reg 1.000000e+02 train accuracy: 0.307122 val accuracy: 0.299000\n",
      "lr 1.000000e-07 reg 1.000000e+03 train accuracy: 0.309878 val accuracy: 0.308000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.369163 val accuracy: 0.391000\n",
      "lr 1.000000e-06 reg 1.000000e-01 train accuracy: 0.354143 val accuracy: 0.333000\n",
      "lr 1.000000e-06 reg 1.000000e+00 train accuracy: 0.365286 val accuracy: 0.351000\n",
      "lr 1.000000e-06 reg 1.000000e+01 train accuracy: 0.355265 val accuracy: 0.347000\n",
      "lr 1.000000e-06 reg 1.000000e+02 train accuracy: 0.365184 val accuracy: 0.355000\n",
      "lr 1.000000e-06 reg 1.000000e+03 train accuracy: 0.365163 val accuracy: 0.363000\n",
      "lr 1.000000e-06 reg 5.000000e+04 train accuracy: 0.270408 val accuracy: 0.265000\n",
      "lr 5.000000e-06 reg 1.000000e-01 train accuracy: 0.306490 val accuracy: 0.300000\n",
      "lr 5.000000e-06 reg 1.000000e+00 train accuracy: 0.321755 val accuracy: 0.340000\n",
      "lr 5.000000e-06 reg 1.000000e+01 train accuracy: 0.348000 val accuracy: 0.321000\n",
      "lr 5.000000e-06 reg 1.000000e+02 train accuracy: 0.345449 val accuracy: 0.326000\n",
      "lr 5.000000e-06 reg 1.000000e+03 train accuracy: 0.291265 val accuracy: 0.288000\n",
      "lr 5.000000e-06 reg 5.000000e+04 train accuracy: 0.186163 val accuracy: 0.171000\n",
      "lr 1.000000e-05 reg 1.000000e-01 train accuracy: 0.335959 val accuracy: 0.335000\n",
      "lr 1.000000e-05 reg 1.000000e+00 train accuracy: 0.331776 val accuracy: 0.318000\n",
      "lr 1.000000e-05 reg 1.000000e+01 train accuracy: 0.316224 val accuracy: 0.303000\n",
      "lr 1.000000e-05 reg 1.000000e+02 train accuracy: 0.261082 val accuracy: 0.248000\n",
      "lr 1.000000e-05 reg 1.000000e+03 train accuracy: 0.224490 val accuracy: 0.209000\n",
      "lr 1.000000e-05 reg 5.000000e+04 train accuracy: 0.194694 val accuracy: 0.223000\n",
      "lr 5.000000e-05 reg 1.000000e-01 train accuracy: 0.325367 val accuracy: 0.319000\n",
      "lr 5.000000e-05 reg 1.000000e+00 train accuracy: 0.286735 val accuracy: 0.287000\n",
      "lr 5.000000e-05 reg 1.000000e+01 train accuracy: 0.330163 val accuracy: 0.314000\n",
      "lr 5.000000e-05 reg 1.000000e+02 train accuracy: 0.276367 val accuracy: 0.301000\n",
      "lr 5.000000e-05 reg 1.000000e+03 train accuracy: 0.215653 val accuracy: 0.213000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.054694 val accuracy: 0.051000\n",
      "lr 5.000000e-04 reg 1.000000e-01 train accuracy: 0.314408 val accuracy: 0.331000\n",
      "lr 5.000000e-04 reg 1.000000e+00 train accuracy: 0.277612 val accuracy: 0.297000\n",
      "lr 5.000000e-04 reg 1.000000e+01 train accuracy: 0.263469 val accuracy: 0.249000\n",
      "lr 5.000000e-04 reg 1.000000e+02 train accuracy: 0.211857 val accuracy: 0.208000\n",
      "lr 5.000000e-04 reg 1.000000e+03 train accuracy: 0.174694 val accuracy: 0.158000\n",
      "lr 5.000000e-04 reg 5.000000e+04 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.391000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cs231n/classifiers/linear_svm.py:82: RuntimeWarning: invalid value encountered in subtract\n",
      "  L = scores - correct_scores + deltas\n",
      "/home/htan/apps/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:68: RuntimeWarning: invalid value encountered in subtract\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7,5e-6,1e-6,5e-5,1e-5,5e-4]\n",
    "regularization_strengths = [5e4,1e3,1e2,1e1,1,1e-1]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "params = [(x,y) for x in learning_rates for y in regularization_strengths]\n",
    "\n",
    "for (lr, reg) in params:\n",
    "    print(lr, reg)\n",
    "    svm = LinearSVM()\n",
    "    svm.train(X_train,y_train,learning_rate = lr,reg = reg,num_iters = 1500,batch_size = 200,verbose = False)\n",
    "\n",
    "    train_y_predict = svm.predict(X_train)\n",
    "    valid_y_predict = svm.predict(X_val)\n",
    "    train_accuracy = np.mean(y_train == train_y_predict)\n",
    "    val_accuracy = np.mean(y_val == valid_y_predict)\n",
    "    results[(lr,reg)] = (train_accuracy,val_accuracy)\n",
    "    if val_accuracy > best_val:\n",
    "        best_val = val_accuracy\n",
    "        best_svm = svm  \n",
    "\n",
    "    print 'validation accuracy: %f' % (val_accuracy, )\n",
    "    \n",
    "#     toc = time.time()\n",
    "#     print 'That took %fs' % (toc - tic)\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print 'lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy)\n",
    "    \n",
    "print 'best validation accuracy achieved during cross-validation: %f' % best_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/htan/apps/anaconda2/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
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PH3cbU6ZMGbZv307BggUDmFn4WbBgATfeeCOxsbEUKFDgnO2pqakcPHiQ1atXc9lll+Vq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jfuFZEHMx32vPKZMcYYY8LCVd7mqQz8rqpJACIyBmiMY8jIhfMuzVD+Z/53V8brezPzdGWlK9AO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a4a0b4310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.364000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print 'linear SVM on raw pixels final test set accuracy: %f' % test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gW62QeHeHvr/BdoKypNsMUhjmqG1iH4y5zsxsBXcrTLU2wTiqjIl64zXLWdyL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a486b9750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in xrange(10):\n",
    "  plt.subplot(2, 5, i + 1)\n",
    "    \n",
    "  # Rescale the weights to be between 0 and 255\n",
    "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "  plt.imshow(wimg.astype('uint8'))\n",
    "  plt.axis('off')\n",
    "  plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** The weight just looks like the given category."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
